Build trading agents that don't blow up when the LLM hallucinates.

A paper-trading agent lost 15% in 60 minutes — it hallucinated a fake Fed press release and shorted the rally. The code was perfect. The intelligence wasn't.

This $199 course teaches Guardian Patterns that wrap LLM trading agents so a single hallucination, prompt-injection, or stale feed can't liquidate you. Production engineering, not signal hunting.

6 modules / 12 lessons:

• M1: Validation loops — pre-execution sanity checks

• M2: Anti-hallucination guards — cross-source confirmation

• M3: Risk-bounded execution — hard stops agent can't override

• M4: Drift detection — when model behavior shifts mid-session

• M5: Replay debugging — every decision auditable

• M6: Deployment — paper → canary → live, no surprises

Includes: 5 Python repos (Binance Testnet), 3 paper backtests with replay logs, validation templates, Discord.

For: engineers (2+ yrs Python), ML engineers shipping first autonomous system, quants adding LLM layers.

Not for: beginners chasing "passive signal bots" or magic indicators. This is how to NOT lose money to your own AI.

Start M1 — 30 min in, you write code that catches what your current setup misses.